Modeling artificial, mobile swarm systems

Abstract

Swarm intelligence is a new research paradigm that offers novel approaches for studying and solving distributed problems using solutions inspired by social insects and other natural behaviors of vertebrates. In this thesis, we present methodologies for modeling artificial mobile systems within the swarm intelligence framework. The proposed methodologies provide guidelines in the study and design of artificial swarm systems for the following two classes of experiments: distributed sensing and distributed manipulation. Event discovery and information dissemination through local communication in artificial swarm systems present similar characteristics as natural phenomena such as foraging and food discovery in insect colonies and the spread of infectious diseases in animal populations, respectively. We show that the artificial systems can be described in similar mathematical terms as those used to describe the natural systems. The proposed models can be classified in two main categories: non-embodied and embodied models. Furthermore, within each category, we distinguish two subcategories: spatial and nonspatial models. In our description of distributed manipulation in swarm robotic systems we present two case studies of non-collaborative and collaborative manipulations, respectively. The general approach proposed here consists of first representing the group behavior of the active agents with a finite state machine then describing mathematically the dynamics of the group. The first case study is the aggregation experiment. We present a macroscopic model that accurately captures the dynamics of the experiment and a suite of threshold-based, scalable, and fully distributed algorithms for allocating the workers to the task optimally. The second case study is that of the stick-pulling experiment. This task requires the collaborative effort of two robots to be successful. Here, we present a discrete-time macroscopic model that helps us uncover counter-intuitive behaviors that result from collaboration between the agents. We complete each proposed modeling methodology by showing how the parameters of the models can be calculated using solely the characteristics of the environment and those of the agents and by analyzing the constraints and limitations of the different models. Finally, we use different tools (simulations and real robots) to validate the proposed models.